Where Data Meets Human Behavior at SocioLogic
Dr. Sarah Chen spent a decade in academia studying how people make decisions before realizing that the real-world applications of her research were being held back by traditional research methods.
After completing her PhD in Behavioral Economics, she led research teams at two Fortune 500 companies where she became increasingly frustrated with the limitations of conventional market research: small sample sizes, slow turnaround times, and the impossibility of asking follow-up questions after a study concluded.
When she discovered the potential of AI-powered synthetic personas, she saw an opportunity to revolutionize how companies understand their customers. At SocioLogic, she leads our research methodology, ensuring that our synthetic users behave in ways that are grounded in actual human psychology.
Sarah's writing tends toward the technical and evidence-based. She frequently references academic studies and isn't afraid to dive deep into methodology. Her articles are favorites among research professionals and data scientists who appreciate her rigorous approach.
When she's not nerding out about research methodology, Sarah enjoys trail running in the Berkeley hills and has an inexplicable obsession with collecting vintage research equipment—her office features a 1960s tachistoscope that she insists still works.
The transport layer works. Everything above it is still being invented.
Model Context Protocol nails tool invocation and streaming. But discovery, payments, verification, and trust are completely absent from the spec. Someone has to build those layers.
Lessons from npm, Docker Hub, and twenty years of package registry mistakes
We're building an agent registry. It's harder than it sounds. Here's what we learned from the package registry world about schema design, verification, and not repeating npm's mistakes.
When agents call agents, every millisecond multiplies
A 200ms API call is fine for a human. But when Agent A calls Agent B calls Agent C, that 200ms becomes 600ms, and your user is staring at a spinner wondering what went wrong.
We stress-tested our persona engine to its limits. It was instructive.
We created 10,000 synthetic buyer personas in a single cohort to find out where our system breaks. It broke in ways we didn't expect.